Significant Deviations Research Articles

Length control emerges from cytoskeletal network geometry

Many cytoskeletal networks consist of individual filaments that are organized into elaborate higher-order structures. While it is appreciated that the size and architecture of these networks are critical for their biological functions, much of the work investigating control over their assembly has focused on mechanisms that regulate the turnover of individual filaments through size-dependent feedback. Here, we propose a very different, feedback-independent mechanism to explain how yeast cells control the length of their actin cables. Our findings, supported by quantitative cell imaging and mathematical modeling, indicate that actin cable length control is an emergent property that arises from the cross-linked and bundled organization of the filaments within the cable. Using this model, we further dissect the mechanisms that allow cables to grow longer in larger cells and propose that cell length-dependent tuning of formin activity allows cells to scale cable length with cell length. This mechanism is a significant departure from prior models of cytoskeletal filament length control and presents a different paradigm to consider how cells control the size, shape, and dynamics of higher-order cytoskeletal structures.

Evidence of spin-phonon interactions in MgCu2O3: low temperature Raman studies

Experimental low temperature Raman spectroscopy studies upto the temperature of 83 K were performed on MgCu2O3 compound using 785 nm excitation source. Experimentally obtained Raman spectra compare well with those obtained from DFPT calculations. Phonon modes show different behaviour below and above the Néel temperature ∼95 K. Anharmonic contribution is prominent above the Néel temperature but a significant departure from the anharmonic behaviour was observed for certain Ag and B3g modes below the Néel temperature. This behaviour is attributed to spin-phonon coupling present in the compound. The spin-phonon coupling arises due to modulation in the exchange integral by the lattice vibrations. The atomic vibrations of each mode and their effect on the exchange integral are discussed.

Nitroxide radical conjugated ovalbumin theranostic nanosystem for enhanced dendritic cell-based immunotherapy and T1 magnetic resonance imaging

Melanoma, known for its aggressive metastatic nature, presents a formidable challenge in cancer treatment, where conventional therapies often fall short. This study introduces a pioneering approach utilizing metal-free nanosystem as tumor vaccines, spotlighting their potential in revolutionizing melanoma treatment. This work employed organic nitroxides, specifically 4-carboxy-TEMPO, in combination with chitosan (CS), to create a novel nanocomposite material - the CS-TEMPO-OVA nanovaccines. This composition not only improves biocompatibility and extends blood circulation time of TEMPO but also marks a significant departure from traditional gadolinium-based contrast agents in MRI technology, addressing safety concerns. CS-TEMPO-OVA nanovaccines demonstrate excellent biocompatibility at both the cellular and organoid level. They effectively stimulate bone marrow-derived dendritic cells (BMDCs), which in turn promote the maturation and activation of T cells. This ultimately leads to a strong production of essential cytokines. These nanovaccines serve a dual purpose as both therapeutic and preventive. By inducing an immune response, activating cytotoxic T cells, and promoting macrophage M1 polarization, they effectively inhibit melanoma growth and enhance survival in mouse models. When combined with αPD-1, the CS-TEMPO-OVA nanovaccines significantly bolster the infiltration of cytotoxic T lymphocytes (CTLs) within tumors, sparking a powerful systemic antitumor response that effectively curbs tumor metastasis. The ability of these nanovaccines to control both primary (subcutaneous) and metastatic B16-OVA tumors highlights their remarkable efficacy. Furthermore, the CS-TEMPO-OVA nanovaccine can be administered in vivo via both intravenous and intramuscular routes, both of which effectively enhance the T1 contrast of magnetic resonance imaging in tumor tissue. This study offers invaluable insights into the integrated application of these nanovaccines in both clinical diagnostics and treatment, marking a significant stride in cancer research and patient care.

Enhancing molecular property prediction with auxiliary learning and task-specific adaptation

Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target task can lead to poor generalization. To address this, we explore the adaptation of pretrained GNNs to the target task by jointly training them with multiple auxiliary tasks. This could enable the GNNs to learn both general and task-specific features, which may benefit the target task. However, a major challenge is to determine the relatedness of auxiliary tasks with the target task. To address this, we investigate multiple strategies to measure the relevance of auxiliary tasks and integrate such tasks by adaptively combining task gradients or by learning task weights via bi-level optimization. Additionally, we propose a novel gradient surgery-based approach, Rotation of Conflicting Gradients (RCGrad\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathop {\ exttt{RCGrad}}\\limits$$\\end{document}), that learns to align conflicting auxiliary task gradients through rotation. Our experiments with state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed methods, with improvements of up to 7.7% over fine-tuning. This suggests that incorporating auxiliary tasks along with target task fine-tuning can be an effective way to improve the generalizability of pretrained GNNs for molecular property prediction.Scientific contributionWe introduce a novel framework for adapting pretrained GNNs to molecular tasks using auxiliary learning to address the critical issue of negative transfer. Leveraging novel gradient surgery techniques such as RCGrad\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathop {\ exttt{RCGrad}}\\limits$$\\end{document}, the proposed adaptation framework represents a significant departure from the dominant pretraining fine-tuning approach for molecular GNNs. Our contributions are significant for drug discovery research, especially for tasks with limited data, filling a notable gap in the efficient adaptation of pretrained models for molecular GNNs.

Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm

Active Noise Control (ANC) systems play a crucial role in reducing unwanted noise in various settings. Traditional ANC methods, like the Filtered-x Least Mean Squares (FxLMS) algorithm, are effective in linear noise scenarios. However, they often struggle with more nonlinear and complex noise patterns. This paper introduces a novel approach using the brain storm optimization (BSO) algorithm in nonlinear ANC systems, which represents a significant departure from conventional techniques. The BSO algorithm, inspired by human brainstorming processes, excels in addressing the complexities of nonlinear noise by incorporating principles, such as delayed evaluation, free imagination, quantity and quality, and comprehensive improvement. By combining the BSO algorithm with an Extended Kalman Filter (EKF), a new ANC system is proposed that can adapt to a wide range of noise types with improved speed and accuracy. Experimental results showcase the superior performance of the BSO algorithm, achieving an impressive noise reduction of up to 48 dB (dB) in a 500Hz sinusoidal noise scenario, with a convergence time as fast as 0.01 s, outperforming the FxLMS algorithm by a significant margin. Moreover, in complex environments with multi-frequency and random noise, the BSO algorithm consistently demonstrates better noise reduction and quicker convergence, reducing noise levels by up to 27 dB within 0.001 s. The innovative use of the BSO algorithm in ANC systems not only enhances noise reduction capabilities, especially for nonlinear and complex noise signals, but also improves convergence times, paving the way for future advancements in ANC technologies.

The End(s) of The Tempest in Post-apartheid South Africa

ABSTRACT Following the sea change that led South Africa into the democratic era in 1994, theatre-makers have explored the resonances and limitations of The Tempest as an expression of local realities. In this article, I provide a partial performance history of the play in this period, tracing the ends (and endings) towards which it has been staged in five productions: two in 1994 at Maynardville Open Air Theatre and Rhodes University respectively, and three more at Durban University of Technology (2003), the University of the Witwatersrand (2004), and the Baxter Theatre (2009). These showed varying creative responses to the text, suggesting a double bind in performance: the network of associations attached to the play are both inescapable and frequently inadequate for articulating complex and variable post-apartheid experiences. Theatre-makers have thus, at times, moved beyond usual boundaries of the text, suggesting a need for something other than The Tempest to give voice to contemporary concerns. This was most fully evidenced by two further productions from the 2010s: Miranda’s Tale (2016), and Kunene and the King (2019). In relation to the preceding productions of The Tempest, these two plays demonstrate significant departures from the text and the politics it has come to represent.

Optimizing the fundamental eigenvalue gap of quantum graphs

Abstract We study the problem of minimizing or maximizing the fundamental spectral gap of Schr"odinger operators on metric graphs with
either a convex potential or 
a ``single-well'' potential on an appropriate specified subset. (In the case of metric trees, such a subset can be the entire graph.) In the convex case we find
that the minimizing and maximizing potentials are piecewise linear with only a finite number of points of non-smoothness, but give examples showing that the optimal potentials need not be constant. This is a significant departure from the usual scenarios on intervals and domains where the constant potential is typically minimizing. In the single-well case we show that the optimal potentials are piecewise constant with a finite number of jumps, and in both cases give an explicit estimate on the number of points of non-smoothness, respectively jumps, the minimizing potential can have. Furthermore, we show that, unlike on domains, it is not generally possible to find nontrivial bounds on the fundamental gap in terms of the diameter of the graph alone, within the given classes.

Large dynamic scissoring mode displacements coupled to band gap opening in the cubic phase of the methylammonium lead halide perovskites

Hybrid perovskites are a rapidly growing research area, having reached photovoltaic power conversion efficiencies of over 25%. There is a increasing consensus that the structures of these materials, and hence their electronic structures, cannot be understood purely from the time and space averaged crystal structures observable by conventional methods. We apply a symmetry-motivated analysis method to analyse x-ray pair distribution function data of the cubic phases of the hybrid perovskites MAPbX 3 (X = I, Br, Cl). We demonstrate that, even in the cubic phase, the local structure of the inorganic components of MAPbX 3 (X = I, Br, Cl), are dominated by scissoring type deformations of the PbX 6 octahedra. We find these modes to have a larger amplitude than equivalent distortions in the A-site deficient perovskite ScF3 and demonstrate that they show a significant departure from the harmonic approximation. Calculations performed on an inorganic perovskite analogue, FrPbBr3, show that the large amplitudes of the scissoring modes are coupled to a dynamic opening of the electronic band gap. Finally, we use density functional theory calculations to show that the organic MA cations reorientate to accommodate the large amplitude scissoring modes.

Finland's Strategic Shift: A New Chapter with NATO Membership

This paper delves into Finland's potential accession to NATO, marking a significant departure from its longstanding policy of military non-alignment. As geopolitical dynamics in the Baltic region undergo profound changes, Finland's consideration of NATO membership carries far-reaching implications for regional security and global geopolitics. Finland responded to Russian aggressiveness by joining the North Atlantic Treaty Organization (NATO) to counteract Russia�s security threats in the region. The abstract explores the historical context of Finland's neutrality, the evolving security landscape, and the factors driving the nation towards reevaluating its defense posture. Using the research method of critical content analysis and in-depth interview with military and defense and security analyst Rahakundini, the potential impact on NATO, Russia's response, and the broader implications for the delicate balance of power in Northern Europe. The proposed NATO membership signals a strategic recalibration for Finland, with ramifications extending beyond national borders, shaping the geopolitical narrative in an ever-evolving international arena.

Luth Badila's Learning Time

Learning media play a crucial role in enhancing the student learning process, with various types of media being employed to engage students and facilitate better understanding. Posters are one such medium that can significantly enhance students' attention and interest during learning sessions. However, traditional posters often lack elements that make them interactive and engaging enough to substantially impact speaking skills and teamwork. This study aims to introduce and evaluate the effectiveness of a novel learning medium, known as Luth-Badila’s Learning Time, which utilizes uniquely designed 3D posters incorporating subject materials, quizzes, and a rotating clock embedded in a robot's head to attract students' attention and improve their speaking skills and teamwork. The implementation of Luth-Badila’s Learning Time in classrooms has shown promising results, with a noticeable increase in student engagement, improved speaking skills, and enhanced teamwork. The novelty of this learning medium lies in its interactive 3D design and the integration of multiple educational elements into a single poster, which is a significant departure from traditional 2D posters. The findings suggest that incorporating such innovative and interactive learning media in educational settings can lead to better student outcomes, particularly in terms of speaking skills and collaborative abilities. This study contributes to the existing body of knowledge by providing evidence of the benefits of using advanced, interactive posters in educational contexts, thereby encouraging educators to adopt similar approaches to enhance learning experiences. Highlights: Innovative Design: Luth-Badila’s Learning Time uses 3D posters with quizzes and a rotating clock to captivate students. Improved Skills: This medium significantly boosts speaking skills and teamwork among students. Educational Impact: Interactive posters lead to higher student engagement and better learning outcomes. Keywords: 3D Poster, Learning Media, Speaking Skills, Student Engagement

Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction

Sensor degradation poses a critical yet ‘often overlooked’ challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity.

Unveiling the digital desire: UTAUT analysis of NFT investment intentions in Malaysia

PurposeThis study pioneers the investigation into the determinants influencing Malaysian investors' intentions towards Non-Fungible Token (NFT) investments, utilizing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. It explores the burgeoning interest in NFTs within the Malaysian market, an emerging economy, and identifies the behavioral adoption determinants critical for NFT investment decisions.Design/methodology/approachAdopting a quantitative methodology, the research engaged 183 experienced Malaysian investors through a structured online questionnaire survey. The study employed regression analysis to assess the impact of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Usefulness, Social Support and Perceived Trust on NFT investment intentions.FindingsThe findings reveal that Performance Expectancy and Social Support significantly predict the intention to invest in NFTs, accounting for 47% of the variance in investment intentions. The study highlights the crucial role of perceived benefits and community support in shaping Malaysian investors' engagement with NFTs, amidst the complexities of the digital asset landscape.Research limitations/implicationsThe study acknowledges the limitation posed by its sampling method and size, suggesting the need for broader investigations that include a more diverse demographic to enhance the generalizability of the findings. Future research could further delve into the specific behaviors, motivations and challenges of NFT investors and creators.Practical implicationsThe significant predictive power of Performance Expectancy indicates a primary financial motivation among Malaysian NFT investors, suggesting policymakers consider regulations that foster innovation and growth in the NFT sector while safeguarding investors. The study also underscores the importance of community support, pointing towards the development of platforms that facilitate knowledge sharing among NFT enthusiasts.Social implicationsBy demonstrating the pivotal role of social support in the NFT investment decision-making process, the research implies a powerful sense of community among investors in the digital asset space. It suggests the potential of NFTs to foster a more inclusive and accessible market for creative industry entrepreneurs, facilitating direct engagement and profit realization.Originality/valueThis research marks a significant departure from existing studies by tailoring the UTAUT model specifically to the NFT investment context in Malaysia. It unveils the nuanced dynamics influencing NFT investment intentions, emphasizing the unique contributions of Performance Expectancy and Social Support, thus providing a fresh perspective on NFT adoption in emerging markets.

Unveiling the digital desire: UTAUT analysis of NFT investment intentions in Malaysia

PurposeThis study pioneers the investigation into the determinants influencing Malaysian investors' intentions towards Non-Fungible Token (NFT) investments, utilizing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. It explores the burgeoning interest in NFTs within the Malaysian market, an emerging economy, and identifies the behavioral adoption determinants critical for NFT investment decisions.Design/methodology/approachAdopting a quantitative methodology, the research engaged 183 experienced Malaysian investors through a structured online questionnaire survey. The study employed regression analysis to assess the impact of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Usefulness, Social Support and Perceived Trust on NFT investment intentions.FindingsThe findings reveal that Performance Expectancy and Social Support significantly predict the intention to invest in NFTs, accounting for 47% of the variance in investment intentions. The study highlights the crucial role of perceived benefits and community support in shaping Malaysian investors' engagement with NFTs, amidst the complexities of the digital asset landscape.Research limitations/implicationsThe study acknowledges the limitation posed by its sampling method and size, suggesting the need for broader investigations that include a more diverse demographic to enhance the generalizability of the findings. Future research could further delve into the specific behaviors, motivations and challenges of NFT investors and creators.Practical implicationsThe significant predictive power of Performance Expectancy indicates a primary financial motivation among Malaysian NFT investors, suggesting policymakers consider regulations that foster innovation and growth in the NFT sector while safeguarding investors. The study also underscores the importance of community support, pointing towards the development of platforms that facilitate knowledge sharing among NFT enthusiasts.Social implicationsBy demonstrating the pivotal role of social support in the NFT investment decision-making process, the research implies a powerful sense of community among investors in the digital asset space. It suggests the potential of NFTs to foster a more inclusive and accessible market for creative industry entrepreneurs, facilitating direct engagement and profit realization.Originality/valueThis research marks a significant departure from existing studies by tailoring the UTAUT model specifically to the NFT investment context in Malaysia. It unveils the nuanced dynamics influencing NFT investment intentions, emphasizing the unique contributions of Performance Expectancy and Social Support, thus providing a fresh perspective on NFT adoption in emerging markets.

A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.

Classical and Lagrangian mechanics in ray tracing: An optimizable framework for inhomogeneous media

Ray tracing is crucial for analyzing wave behaviors in diverse applications. This study presents an approach that extends beyond traditional methods, which typically involve solving second-order differential equations to determine ray trajectories. Leveraging classical momentum-impulse relations and the system’s Lagrangian, we establish a set of intuitive first integrals that circumvent the need for direct differential solutions, paving the way for optimization techniques. Our method employs the “shooting method” a technique for approximating solutions to differential equations by iteratively applying initial conditions. We introduce a novel momentum cost function that streamlines angle determination in anisotropic environments, a significant departure from conventional practices. Numerical validations demonstrate the robustness of our approach, confirming its efficacy in handling both sharp and gradual refractive index changes with high accuracy. The results also highlight the computational intensity required in anisotropic conditions, suggesting potential areas for efficiency improvements. This groundwork not only enhances current understanding but also opens avenues for future research into more complex media.

Chemical Kinetics and Thermal Properties of Ablator Pyrolysis Products During Atmospheric Entry

Legacy and modern-day ablation codes typically assume equilibrium pyrolysis gas chemistry. Yet, experimental data suggest that speciation from resin decomposition is far from equilibrium. A thermal and chemical kinetic study was performed on pyrolysis gas advection through a porous char, using the Theoretical Ablative Composite for Open Testing (TACOT) as a demonstrator material. The finite-element tool SIERRA/Aria simulated the ablation of TACOT under various conditions. Temperature and phenolic decomposition rates generated from Aria were applied as inputs to a simulated network of perfectly stirred reactors (PSRs) in the chemical solver Cantera. A high-fidelity combustion mechanism computed the gas composition and thermal properties of the advecting pyrolyzate. The results indicate that pyrolysis gases do not rapidly achieve chemical equilibrium while traveling through the simulated material. Instead, a highly chemically reactive zone exists in the ablator between 1400 and 2500 K, wherein the modeled pyrolysis gases transition from a chemically frozen state to chemical equilibrium. These finite-rate results demonstrate a significant departure in computed pyrolysis gas properties from those derived from equilibrium solvers. Under the same conditions, finite-rate-derived gas is estimated to provide up to 50% less heat absorption than equilibrium-derived gas. This discrepancy suggests that nonequilibrium pyrolysis gas chemistry could substantially impact ablator material response models.

Multiculturalism, Islamophobia and the Muslim Minority in Australia: A Reflection

Australia’s approach to multiculturalism has been a journey of evolving policies and societal shifts. The country’s immigration policies in the 1960s marked a significant departure from the restrictive ‘White Australia’ policy, leading to a more diverse cultural, ethnic, and religious population. Recognizing the potential for value and norm conflicts, the Australian government adopted multiculturalism as a state ideology, aiming to foster social cohesion and reduce inter-community conflicts. This policy has been largely successful in managing conflicts, as evidenced by the relatively low incidence of political, ethnic, and religious violence, including terrorism, compared to other nations. The positive impact of multiculturalism is evident in the thriving Australian Muslim communities, which, along with other ethnic and religious groups, have been able to preserve their cultural heritage. These communities contribute significantly to the social and economic fabric of the nation. However, despite the overarching success of multiculturalism, challenges persist. The Muslim minority in Australia has faced Islamophobia, manifesting in both overt attacks and subtle forms of discrimination. This has been a complex issue, with some Muslim groups maintaining a degree of exclusivity and engaging in identity politics, while also recognizing and embracing the broader multicultural ethos that has become an integral part of Australia’s identity. The Australian government has continued to refine its multicultural policies, with initiatives aimed at promoting anti-racism, access, and equity since 2010. These efforts are part of a broader commitment to diversity, belonging, and inclusion, which encompasses Aboriginal and Torres Strait Islander peoples as well. The policy landscape is complemented by state and territory policies that further support multiculturalism. Despite the progress, the experiences of Australian Muslims highlight the ongoing need for vigilance against discrimination. Reports indicate that Australian Muslims are a diverse group, facing stereotypes and prejudice that can perpetuate social tensions. The Australian Human Rights Commission has emphasized the importance of sharing the stories of Australian Muslims to foster understanding and address Islamophobia. The Commission’s findings underscore the need for a national anti-racism framework and strong leadership to combat discrimination and support social cohesion. While Australia’s multicultural policy has been a beacon of successful integration and social harmony, it is clear that the journey is not complete. The nation continues to grapple with the dual realities of a celebrated multicultural society and the undercurrents of intolerance that challenge it. The experiences of the Australian Muslim community serve as a reminder of the work that remains to be done in ensuring that multiculturalism is not just a state ideology but a lived reality for all Australians. The path forward lies in education, awareness, and institutional change that upholds the values of diversity and inclusion for every individual in the nation.

The nasoalveolar molding technique versus DynaCleft nasal elevator application in infants with unilateral cleft lip and palate.

The introduction of presurgical nasoalveolar molding represented a significant departure from traditional molding methods. Developed by Grayson and colleagues in 1993, this technique combines an intraoral molding device with a nasal molding stent. This study aimed to compare the Grayson nasoalveolar molding appliance versus DynaCleft appliance as two methods of presurgical nasoalveolar molding. A single-blinded, randomized, parallel-arm clinical trial was conducted. Sixteen infants with complete unilateral cleft lip and palate were enrolled and divided into two groups of eight. Group 1 was treated with a modified Grayson nasoalveolar molding appliance that included a nasal stent, while group 2 was treated with DynaCleft elastic adhesive tape and an external nasal elevator. Standardized digital photographs of each infant were taken at baseline and post-treatment using a professional camera. Nine extraoral anthropometric measurements were obtained from each image using image measurement software. The modified Grayson nasoalveolar appliance demonstrated a more significant improvement compared to DynaCleft in terms of alar length projection (on both sides), columella angle, and nasal tip projection. Symmetry ratios also showed enhancement, with significant improvements observed in nasal width, nasal basal width, and alar length projection (p< 0.05). Both the modified Grayson nasoalveolar appliance and DynaCleft appear to be effective presurgical infant orthopedics treatment options, demonstrating improvements in nasolabial aesthetics. The modified Grayson appliance, equipped with a nasal stent, improved nasal symmetry more effectively than DynaCleft, resulting in a straighter columella and a more medially positioned nasal tip.

Genetic Relationships of Cultivated Flax and Its Wild Progenitor as Revealed by 454 Pyrosequencing, Sanger Resequencing and Microsatellite Data

Flax (Linum usitatissimum L.), as the earliest oil and fiber crop, is a model plant for genetic inferences of plant domestication processes involving multiple domestication events. However, a puzzle has emerged from several genetic studies, as dehiscent cultivated flax is genetically more related to its progenitor pale flax (L. bienne Mill.), and winter cultivated flax is well mixed with oil or fiber cultivated flax, while capsular dehiscence and winter hardiness are the major characteristics of pale flax. For this, a comparative analysis was conducted with 16 Linum samples representing pale flax and four domestication groups of cultivated flax (oil, fiber, winter, and dehiscent) using 454 pyrosequencing, Sanger resequencing and microsatellite data. It was found that the genomic sampling of genetic variants from the three applied methods yielded similar genetic information on pale flax and four groups of cultivated flax. The revealed genetic relationships did not show significant departures from the previous findings, but instead supported an early, independent domestication of a primitive flax lineage for oil use, followed by a subsequent flax domestication process with multiple domestication events for capsular dehiscence, oil, fiber and winter hardiness. Domestication on capsular dehiscence occurred earlier than domestication on winter hardiness. Domestication on winter hardiness was more complicated than domestication on capsular dehiscence.

Efficient packaging defect detection: leveraging pre-trained vision models through transfer learning

The inspection of packaging defects is a crucial aspect of maintaining the quality of industrial production, especially in the case of boxed products. This study introduces a novel approach for detecting physical defects in product packaging boxes by integrating image processing with deep learning, specifically transfer learning with two images as an input. The proposed method utilizes both top view and side view images of the packaging to determine its condition, a significant departure from the conventional single image input. Our approach incorporates 16 pre-trained model variants from EfficientNetV2, MobileNetV3, and ResNetV2 for transfer learning as feature extractors. The experimental findings demonstrate that the best model that leverages EfficientNetV2 variant achieves 100% accuracy and F1 score in terms of classification performance. However, the most optimal model in terms of classification performance and inference speed was the one that leveraged ResNetV2 variant. This model scored 95% accuracy and 95.24% F1 score, with an inference speed of 91 ms per prediction.